sklearn.decomposition.RandomizedPCA 找不到了 如何解决

新问题的发现

sklearn.decomposition.RandomizedPCA 仅仅是0.17的版本的。后续的版本都没有了。

http://lijiancheng0614.github.io/scikit-learn/modules/generated/sklearn.decomposition.RandomizedPCA.html

解决方法：

from sklearn.decomposition import PCA
# Create a Randomized PCA model that takes two components
randomized_pca = PCA(n_components=2,svd_solver='randomized')

为什么：
文档0.21.2
https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html

全部组合成为了一个 PCA

>>> import numpy as np
>>> from sklearn.decomposition import PCA
>>> X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]])
>>> pca = PCA(n_components=2)
>>> pca.fit(X)  
PCA(copy=True, iterated_power='auto', n_components=2, random_state=None,
  svd_solver='auto', tol=0.0, whiten=False)
>>> print(pca.explained_variance_ratio_)  
[0.9924... 0.0075...]
>>> print(pca.singular_values_)  
[6.30061... 0.54980...]

>>> pca = PCA(n_components=2, svd_solver='full')
>>> pca.fit(X)                 
PCA(copy=True, iterated_power='auto', n_components=2, random_state=None,
  svd_solver='full', tol=0.0, whiten=False)
>>> print(pca.explained_variance_ratio_)  
[0.9924... 0.00755...]
>>> print(pca.singular_values_)  
[6.30061... 0.54980...]

>>> pca = PCA(n_components=1, svd_solver='arpack')
>>> pca.fit(X)  
PCA(copy=True, iterated_power='auto', n_components=1, random_state=None,
  svd_solver='arpack', tol=0.0, whiten=False)
>>> print(pca.explained_variance_ratio_)  
[0.99244...]
>>> print(pca.singular_values_)  
[6.30061...]